code: def Kuf_sqexp_multiscale(
      inducing_variable: Multiscale,
      kernel: SquaredExponential,
      Xnew: TensorType,
  ) -> tf.Tensor:
      Xnew, _ = kernel.slice(Xnew, None)
      Zmu, Zlen = kernel.slice(
          inducing_variable.Z, inducing_variable.scales
      )
      idlengthscales = kernel.lengthscales + Zlen
      d = inducing_variable._cust_square_dist(
          Xnew, Zmu, idlengthscales[None, :, :]
      )
      lengthscales = tf.reduce_prod(
          kernel.lengthscales / idlengthscales, 1
      )
      lengthscales = tf.reshape(lengthscales, (1, -1))
      return tf.transpose(
          kernel.variance * tf.exp(-0.5 * d) * lengthscales
      )
  
file_path: GPflow/gpflow/covariances/kufs.py
imports: import numpy
  import tensorflow
  from check_shapes import check_shapes
  from ..base import TensorLike, TensorType
  from ..inducing_variables import InducingPatches, InducingPoints, Multiscale
  from ..kernels import Convolutional, Kernel, SquaredExponential
  from .dispatch import Kuf
label_desc: Use image node_id 2 for calling a global function with example usage: Kuf_sqexp_multiscale(inducing_variable, kernel, Xnew) and returns: tf